The mechanism of liver X receptor regulates the balance of glycoFAsynthesis and cholesterol synthesis in clear cell renal cell carcinoma

. From the perspective of bioinformatics and metabolomics, the results suggest that glycoFAsynthesis and cholesterol biosynthesis have great changes in ccRCC. These two changes are likely to have an important impact on the occurrence and development of ccRCC.

glycoFAsynthesis, cholesterol, and mixed subgroups. We explored genetic differences, drug sensitivity, and clinical outcomes in these subgroups. The heat map showed upregulated glycoFAsynthesis genes in glycoFAsynthesis and mixed subgroups, while cholesterol synthesis genes were highly expressed in cholesterol and mixed subgroups ( Figure 1N and Table S2). Based on pathological features and survival information of ccRCC patients, we developed a corresponding survival curve. The glycoFAsynthesis subgroup had the worst prognosis among almost all pathological types ( Figure 1O-Q and Figure S1A-F). In contrast, the performance of the cholesterol synthesis subgroup was distinct. For instance, the cholesterol synthesis subgroup in the T1 and T2 groups demonstrated a better prognosis, whereas the cholesterol synthesis subgroup in the T3 and T4 groups exhibited a poor prognosis. Cholesterol synthesis had significantly different prognostic implications for patients in the early and late stages of ccRCC. Drug sensitivity analysis from the GDSC database showed that pazopanib, temsirolimus, lapatinib, and bosutinib were more effective in the glycoFAsynthesis subgroup, while rapamycin, vinblastine, gefitinib, and metformin were more effective in the cholesterol subgroup ( Figure 1R-Y). Submap analysis indicated that the cholesterol subgroup may be more responsive to CTLA-4 inhibitors (p-value = .01) ( Figure 1Z). Liver X receptor (LXR) molecules play a vital role in glucose and lipid metabolism, as well as cholesterol metabolism. To study the relationship between gly-coFAsynthesis, cholesterigenic pathways, and LXR, we classified LXR into NR1H2 (LXRβ) and NR1H3 (LXRα) subtypes. 7 Heat maps showed a strong positive correlation between glycoFAsynthesis and NR1H2 (Figure 2A,B). Multivariate GSEA revealed that NR1H2 was related to osteoblast differentiation and regucalcin activation in proximal tubule epithelial kidney cells ( Figure 2C), while NR1H3 was linked to endothelin pathways and regulation of the actin cytoskeleton ( Figure 2D). Violin diagrams  that the enrichment scores of NR1H2 and NR1H3 in the four subgroups according to the cholesterol synthesis pathway were significantly lower in the cholesterol subgroup than in the glycoFAsynthesis subgroup ( Figure 2E,F). To investigate LXR's role in ccRCC, ACHN renal cancer cells were treated with LXR's inverse agonist SR9243. Sequencing results showed differential expression of many genes in the SR9243 group compared to the control group (Table S3). The differentially expressed genes were subjected to gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. BP analysis results indicated that these differentially expressed genes (DEGs) were associated with RNA catabolic process and translational initiation. Furthermore, DEGs were associated with focal adhesion and cell-substrate junction, according to CC analysis. In addition, DEGs were associated with cadherin binding and cell adhesion molecule binding according to MF analysis ( Figure 2G). KEGG analysis showed enrichment in amyotrophic lateral sclerosis and human papillomavirus infection ( Figure 2H). GSVA analysis suggested SR9243 may trigger mTORC1 and TNF-α signalling via NF-κB and MYC targets v1 ( Figure 2K). Heatmaps and a volcano map were generated to display differentially expressed genes related to cholesterol transport, glycolysis, fatty acid synthesis, and cholesterol synthesis ( Figures 2I,J). A potential mechanism was proposed, demonstrating how the LXR inverse agonist SR9243 exerts biological effects in ccRCC by regulating glucose and lipid metabolism using differentially expressed genes ( Figure 2L). In vivo experiments were done with LXR agonist LXR623 in nude mice, and the treatment group showed a significant reduction in tumour weight and volume ( Figure 2N-Q). Then, LXR623 was used to treat ACHN renal cancer cells, resulting in differentially expressed genes compared to the control (Table S4). GO and KEGG analyses of DEGs revealed that BP analysis showed links to the regulation of cell cycle phase transition and organelle fission. CC analysis revealed links to focal adhesion and cell-substrate junction, while MF analysis showed links to cadherin binding and DNA-binding transcription factor binding ( Figure 2R). KEGG analysis showed relevance to amyotrophic lateral sclerosis and Huntington's disease ( Figure 2S). GSVA analysis results suggested LXR623's role in ccRCC by activating MYC targets v1 and E2F targets ( Figure 2V). Additionally, a heatmap displaying the expression of cholesterol transport, glycolysis, fatty acid synthesis, and cholesterol synthesisrelated genes before treatment with LXR623 and control groups was generated ( Figure 2T-X). A volcano map displaying the differentially expressed genes was also generated ( Figure 2Y). Finally, a potential mechanism of LXR623 was proposed in regulating glucose and lipid metabolism through differentially expressed genes ( Figure 2M), suggesting LXR623 promotes cholesterol efflux and glycolipid synthesis.
The study used the mulberry map to show the relationship between gene expression, LXR drugs, and key pathways (glycolysis, fatty acid synthesis, cholesterol transport and cholesterol synthesis) ( Figure 3A). Four-quadrant diagram highlighted genes associated with fatty acid synthesis and cholesterol transport ( Figure 3B). The rainfall plot identified NUP210, SCD and ABCG1 as key players in ccRCC lipid metabolism ( Figure 3C). The schematic diagram showed LXR drugs inducing cell death by disrupting biomembrane integrity ( Figure 3D). Furthermore, a Venn diagram was utilized to identify the genes involved in the lipid synthesis pathway of biomembranes and the DEGs caused by SR9243 and LXR623 treatment. The analysis revealed that A4GALT, GAL3ST1, SPTLC2, ST3GAL2, B4GALT5 and ABCA2 were shared by all three gene sets ( Figure 3E). The heat map showed these six genes had low expression in both LXR drug groups ( Figure 3F,G). LXR modulation could be a potential treatment strategy for ccRCC by inhibiting biomembrane synthesis.
In conclusion, our study utilized the glycoFAsynthesischolesterol synthesis axis to identify novel ccRCC subtypes, providing valuable data for future research.  biosynthesis pathway, glycolysis pathway, fatty acid synthesis pathway, and cholesterol transport pathway in ccRCC. Among them, "C" means cholesterol biosynthesis pathway, "G" means glycolysis pathway, "F" means fatty acid synthesis pathway, and "CT" means cholesterol transport pathway. (Y) The volcano plot shows the differentially expressed genes between the LXR623 treatment group and the negative control group.

F I G U R E 3 (A)
The Sankey diagram shows the interrelationship between multiple factors (hub genes, four biological pathways, and drugs specifically targeting liver X receptor [LXR]). (B) The four-quadrant graph shows the relationship between the differentially expressed genes (x-axis) of the LXR623 treatment group and the differentially expressed genes (y-axis) of the SR9243 treatment group, more intuitively. (C) The raincloud plot shows the different importance of these hub genes in the occurrence and development of clear cell renal cell carcinoma (ccRCC). (D) The schematic diagram shows the biological mechanism of the negative control group, the SR9243 treatment group and the LXR623 treatment group. (E) Venn diagram of the overlapping relationship between the two sets of differentially expressed genes and genes related to the membrane lipid biosynthesis process. (F) Heat map shows the expression of these six key genes between the LXR623 treatment group and the negative control group. (G) Heat map shows the expression of these six key genes between the SR9243 treatment group and the negative control group.
of Hakimi et al. In the subsequent cell experiment verification part, whether the selected cell line ACHN can represent ccRCC remains controversial. Our findings suggest the potential of personalized medicine to improve outcomes and call for further investigation into metabolic dysregulation mechanisms in ccRCC.

A C K N O W L E D G E M E N T S
We express our gratitude to the Cancer Genome Atlas (TCGA) for generously providing publicly accessible data, and to the Cancer Imaging Archive (TCIA) for providing publicly available CT images.